Indeed, I suspect the approaches/algorithms for solving a Rubik's cube "compress" a lot better than trying to distill the entire search space in order to be able to predict the exact next move.
I see this trope fairly often, i.e. the assumption that an LLM would need to have been trained on <exact thing it is being asked to solve>. Now, while I do have a moderate amount of background in AI, I am definitely not an expert on LLMs as such. I would be interested to hear someone's take, who does work actively in LLM research. Can they generalise "well enough"? They certainly seem to be able to do so, from my anecdata, and I don't believe "training explicitly for every possible scenario" would have scaled even to today's state.
> Somebody on the forums posted a tool that converts generic joystick axes to keypresses, but not sure how well that works.
I don't have a TSC-X, but did frown a little bit that there wasn't a generic support for controllers with plain axes. I have a VKB STECS, and basically a 85% of train sims need a workaround.
The good news: Claude Code threw together a working prototype for me in a few minutes that had me mapping the two axes (power / brakes) perfectly. It's really a low barrier to entry these days. Can confirm that this game is amazing with it.
Somewhere around the initial ChatGPT release, I was experimenting with having the model draw various types of ASCII art. I believe the first time I ran into a doom loop might have been when I asked it to draw a giraffe; it started with a head, then a line of | | for the neck, and then it just repeated that, ad infinitum. I am not really sure what the cause was, but clearly it wanted to make a long-ish neck, and ran out of a way to determine how many lines it had already produced, so just kept going.
If we're going with a math analogy, I guess it's a bit like teaching them integrals in 3rd grade. You can do it, they probably have the raw IQ for it. But they won't really understand and appreciate it at a deep level (this is even a problem for people when they encounter integrals at the end of high school / early uni).
Novels like these need some life experience to really shine. A 13 year old isn't going to go "how does this writer see so clearly through so many of life's finer details", because they have never experienced 90% of what's being talked about.
Aside from the pragmatic reasons, I think it's a good idea to separate out cardio, muscle-building, and flexibility into its own separate categories, and ensure you consistently dabble in each. Obviously there can be are overlaps, but this taxonomy ensures a good balance.
I don't see this as a binary thing. Legally we tend to draw a clear line between child and adult for pragmatic purposes, but I don't think my responsibility of intent disappears just because someone hits a magical number. I have steered clear of various gambling / "gaming" jobs which have had silly high salaries as a result; I don't in any way want to participate in things which are meant to play the weak points of the human psyche like a harp, for profit.
To be fair, this topic seems to be quite divisive, and seems like something that definitely should be discussed during an interview. Who is right and wrong is one thing, but you likely don't want to be working for a company who has an incompatible take on this topic to you.
I find the chess comment fairly weak; while chess is a "perfect information" game (in that you have full information about every piece on the board), at every level that chess is played at there will be unknowns about the player you are facing.
At lower levels, you can certainly play sub min-maxed moves that are more likely to confuse or pressure a weaker opponent. You could call these bluffs. Opening theory is also a wonderful game, you're essentially playing a game of "let's bet that you haven't prepared this particular set of lines as well as I have" with the opponent. The same thing goes for game styles, open vs closed, etc.
Last but not least, playing a perfect game of chess is so far out of the realm of possibility for humans, that the entire "there's always a correct move" is completely irrelevant. We are now at a point where 7-piece endgames have been completely solved, and it involves 4.2×10^14 positions. Good luck memorizing that, and the scaling from there on out is not pretty.
In this sense, chess occupies a very interesting spot; somewhat calculatable for a human (and yes, tactics dominate up to say 2000 ELO), but there's plenty of room for creativity and strategy also. It's also played at an insanely high level, which makes it a worthwhile challenge and time investment. What it does NOT have is randomness. I often wonder what the competitive landscape of a chess variant involving some randomness would look like, or if it would fundamentally change the nature of the game.
I completely agree, it's fantastic both for general fitness, and as a way of exploring. I do this on a smaller scale on a daily basis, walking the 8-10 km route to or from my work (when we have office days). This is walkable in about 1.5 hours, public transport would get me home in around 45 minutes, so I am not really investing much extra time. Varying the route slightly keeps things interesting, and you get a surprising variety with small (1 to 2 street) changes.
Another favourite of mine is cycling around a neighbourhood to get to know it; you get a totally different feel for things than from a car - things typically go by slower, and you are somehow just far more able to observe things.
Availability is definitely a factor, but I feel that a far more important aspect is that a YouTube feel is personalised. It's A/B testing you for weeks on end, and has a pretty good idea of how to get maximum engagement. TV was never this targeted, nor was there feedback to ratchet up what it suggested to you.
Responsive layout would be the biggest reason (mobile for one, but also a wider range of PC monitor aspect ratios these days than the 4:3 that was standard back then), probably followed by conflating the exact layout details with the content, and a separation of concerns / ease of being able to move things around.
I mean, it's a perfectly viable thing if these are not requirements and preferences that you and your system have. But it's pretty rare these days that an app or site can say "yeah, none of those matter to me the least bit".
I think 5-10% is pretty good, it probably means that the codebase is mostly understandable and maintainable. I have definitely worked on some which were full of little traps and landmines just waiting for eager do-gooders to step on, which was sadly a self-fulfilling prophecy for the app.
I've heard this argument before from the perspective of C# having more keywords and language features to be aware of than something else (in my particular argument, the other side was Java).
From this perspective, I can't say I disagree as such. If you look at the full set of language features, it sure is a lot of stuff to know about. The argument that it is too much, and that we should sacrifice expressiveness and signal to noise ratio in the code to keep the language simpler, I don't agree with.
While I certainly had the _concept_ of compound interest taught to me at some abstract mathematical level, the application to real life practical financial scenarios was definitely not done [1]. Economics as a whole was an optional subject.
I think schools and curriculums could do a whole lot better in representing this important facet of life. More broadly, I often feel that "applying all that math you've learned to real things" is a subject that could be taught.
[1] Seriously, having applied math questions like "Johnny earns X per year, with a cost of living of Y. Assuming inflation of Z and average yearly returns of R, what percentage should he be putting away, starting at age 25, so that at age 50 he essentially gets the equivalent of his own salary each month?" would likely cause some lightbulbs to go off in the kids' heads.
> Nobody had to teach me that delicious things include Oreos and not cardboard.
Well, no, that came from billions of years of pre-training that just got mostly hardcoded into us, due to survival / evolutionary pressure. If anything, the fact that AI is as far as it is, after less than 100 years of development, is shocking. I recall my uncle trounce our C64 in chess, and go on to explain how machines don't have intuition, and the search space explodes combinatorically, which is why they will never beat a competent human. This was ~10 years before Deep Blue. Oh, sure, that's just a party trick. 10 years ago, we didn't have GPT-style language understanding, or image generation (at least, not widely available nor of middling quality). I wonder what we will have in 10, 20, 100 years - whatever it is, I am fairly confident that architectural improvements will lead to large capability improvements eventually, and that current behavior and limitations are just that, current. So, the argument is that somehow, intuitively they can't ever be truly intelligent or conscious because it's somehow intuitively obvious? I disagree with this argument; I don't think we have any real, scientific idea of what consciousness really is, nor do we have any way to differentiate "real" from "fake".
On the other end of the spectrum, I have seen humans with dementia not able to make sense of the world any more. Are they conscious? What about a dog, rabbit, cricket, bacterium? I am pretty sure at their own level, they certainly feel like they are alive and conscious. I don't have any real answers, but it certainly seems to be a spectrum, and holding on to some magical or esoteric differentiator, like emotions or feelings, seems like wishful thinking to me.
> First point: if you imagine that the brain is doing something like collapsing the quantum wavefunction, wouldn't you say that this is a functionally relevant difference in addition to an ontologically relevant difference?
I can imagine a lot of things, but the argument did not go this far, it left it as "obvious" well before this stage. Also, when I see trivial simulations of our biological machinery yielding results which are _very similar_, e.g. character or shape recognition, I am left wondering if the people talking about quantum wavefunctions are not the ones that are making extraordinary claims, which would require extraordinary evidence. I can certainly find it plausible that these _could_ be one particular way that we could be superior to the electronics / valves of the argument, but I'm not yet convinced it is a differentiator that actually exists.
I see this trope fairly often, i.e. the assumption that an LLM would need to have been trained on <exact thing it is being asked to solve>. Now, while I do have a moderate amount of background in AI, I am definitely not an expert on LLMs as such. I would be interested to hear someone's take, who does work actively in LLM research. Can they generalise "well enough"? They certainly seem to be able to do so, from my anecdata, and I don't believe "training explicitly for every possible scenario" would have scaled even to today's state.